Satellite-based meteorological drought indicator to support food security in Java Island

A meteorological drought refers to reduced rainfall conditions and is a great challenge to food security. Information of a meteorological drought in advance is important for taking actions in anticipation of its effects, but this can be difficult for areas with limited or sparse ground observation data available. In this study, a meteorological drought indicator was approached by applying the Standardized Precipitation Index (SPI) to satellite-based precipitation products from multiple sources. The SPI based meteorological drought analysis was then applied to Java Island, in particular to the largest rice-producing districts of Indonesia. A comparison with ground observation data showed that the satellite products accurately described meteorological drought events in Java both spatially and temporally. Meteorological droughts of the eight largest rice-producing districts in Java were modulated by the natural variations in El Niño and a positive-phase Indian Ocean Dipole (IOD). The drought severity was found to be dependent on the intensity of El Niño and a positive-phase IOD that occurs simultaneously, while the duration seems to be modulated more by the positive-phase IOD. The results demonstrate the potential applicability of satellite-based precipitation monitoring to predicting meteorological drought conditions several months in advance and preparing for their effects.

The authors have declared that no competing interests exist.

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A meteorological drought refers to reduced rainfall conditions and is a great challenge 23 to food security. Predicting a meteorological drought in advance is important for taking 24 actions in anticipation of its effects, but this can be difficult for areas with limited or sparse 25 ground observation data available. In this study, a meteorological drought indicator was 26 developed that applies the Standardized Precipitation Index (SPI) to satellite-based 27 precipitation products from multiple sources. The developed indicator was then applied to 28 Java Island, which has the largest rice-producing districts of Indonesia. A comparison with 29 ground observation data showed that the satellite products accurately described 30 meteorological drought events in Java both spatially and temporally. Meteorological droughts 31 were found to be strongly dependent on the natural variations in El Niño and a positive-phase 32 Indian Ocean Dipole (IOD). The drought severity was found to be dependent on the intensity 33 of El Niño and a positive-phase IOD that occurs simultaneously, while the duration seems to 34 be modulated more by the positive-phase IOD. The results demonstrate the potential 35 applicability of satellite-based precipitation monitoring to predicting meteorological drought 36 conditions several months in advance and preparing for their effects. 37

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In Indonesia, various regions have reported drought events indicated by a scarcity of 43 surface water [1,2], which may be followed by depletion of the shallow groundwater table [3] 44 and reduced crop productivity [4,5]. These events are mainly induced by the high seasonality 45 of the rainfall, which is particularly erratic for regions located farthest south from the Equator 46 such as Java, Nusa Tenggara, and the southern parts of Sumatra, Kalimantan, and Papua [6,7]. 47 Drought events in Indonesia are closely related to global climate phenomena, particularly El 48 Niño [6,8]  A meteorological drought refers to a temporary reduction in the amount of 57 precipitation compared to the average [10,11]. The degree of the drought is determined 58 according to the ratio between the amount of precipitation and the amount considered normal 59 for that period, and the duration is determined by how long the dry condition persists. A 60 meteorological drought affects the agricultural sector by causing a groundwater deficit, which 61 can result in crop failure, and reducing the water reserves of dams available for irrigation. If a 62 meteorological drought persists, a hydrological drought may occur, which is defined as a 63 reduced supply of surface water and groundwater and is based on the measured water levels 64 of rivers, reservoirs, lakes, and groundwater [12]. BMKG defines a meteorological drought as 65 an area experiencing dry conditions for a certain time due to reduced precipitation or a longer 66 More recent citations should be included, see few below; https://doi.org/10.1016/j.heliyon.2019.e02148 https://iopscience.iop.org/article/10.1088/1755-1315/648/1/012130/meta https://doi.org/10.1007/s11069-020-04421-x dry season than normal, and it issues warnings based on the number of consecutive days 67 without rain (i.e., dry spell) and prediction of a low precipitation amount. Predicting dry spells 68 is very important because they have a major influence on agriculture, especially rice 69 production [13,14]. Prolonged dry spells can reduce the yield [15] and even the overall rice 70 production [16]. Dry spells are the most sensitive indicator of the effect of El Niño on 71 Indonesia [17]. 72 BMKG monitors for meteorological droughts on a 10-day timescale, which is 73 complemented by near-real-time monitoring of the daily precipitation product estimated from 74 Global Satellite Mapping of Precipitation (GSMaP) data. However, the utilization of available 75 multisource or selected satellite precipitation products is an ongoing challenge that needs to 76 be addressed to improve the comprehensiveness, speed, and accuracy of drought information 77 services in the spatial and temporal domains. Using satellite products for near-and post-real-78 time precipitation estimation can greatly improve the quality of drought analysis. Several In this study, we developed a meteorological drought indicator that applies the widely 103 used Standardized Precipitation Index (SPI) to precipitation products from multiple satellite 104 data sources. This differs from currently available where the SPI is calculated based on sparse 105 surface observation data. The evaluation of multi-source SPI with El Niño and IOD 106 simultaneously is expected to provide a better, optimal, and more accurate representation and 107 understanding of meteorological drought events in the study area. We used the proposed 108 indicator to evaluate the satellite precipitation products against ground observation data for 109 Java Island, with a focus on eight of the 10 largest rice-producing districts of Indonesia: 110 Karawang, Subang, Indramayu, Cilacap, Grobogan, Sragen, Ngawi, and Lamongan (Fig 1). 111 These districts have a very low density of rain gauges and other surface meteorological 112 observation networks. Accurate forecasting of meteorological drought conditions for these 113 districts is fundamental for food security by allowing countermeasures to be performed early 114 in advance. This study may also help address the effects of climate change on Java, which has 115 6 experienced a continuous decrease in precipitation during the dry season over the last three 116 decades  [32]. 117  What is the identified gap that this study is filling? this is not clearly stated, address this during revision

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TRMM has three types of data products: orbital (also known as swath), gridded, and 136 TRMM-related (e.g., ancillary products, ground-based instrument products, TRMM and 137 ground observation subsets, and field experiment products) [36]. In this study, we used a 138 gridded data product: the TRMM (TMPA) 3B43 V7 Monthly dataset for the period March 139 1998-January 2020. TMPA 3B43 is a widely used dataset because of its high spatial and 140 temporal resolutions. The latest version (Version 7) includes a uniform data reprocessing and 141 calibration scheme and a single use of Global Precipitation Climatology Centre monthly rain 142 gauge analysis, which improves the accuracy compared to previous versions [28]. 143

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We used the SPI to determine meteorological drought conditions in the study area. 170 The SPI is commonly used for meteorological drought analysis and can be calculated for 171 various time scales to evaluate not only the water supply in the short term but also the 172 available water resources in the long term [46,47]. McKee et al. developed the SPI in 1993 173 based on their understanding that a precipitation deficit has different effects on the 174 groundwater, reservoir storage, soil moisture, snowpack, and streamflow [10,48]. The SPI is 175 computed by measuring precipitation anomalies at a given location, which are identified by 176 comparing the observed total precipitation with the long-term historical record for a period of 177 interest (e.g., 1, 3, 12, or 49 months) [49]. The historical record is fitted to a gamma 178 probability distribution, which is then transformed into a normal distribution such that the 179 mean SPI for the given location and period is zero. The SPI is calculated as follows (1) 181 where x is the monthly precipitation series, t is the yearly index, and m is a specific month 182 (e.g., January, February). 183 9 For a given location, a rainfall deficit (i.e., meteorological drought) occurs when the 184 SPI is less than -1.0, while excess rainfall occurs when the SPI is greater than 1.0 (Table 2). 185 Because the SPI is given in units of standard deviation from the long-term mean, it can be 186 used to compare precipitation anomalies for any geographic location and any number of 187 timescales. In this study, we used the 3-month (SPI-3) and 6-month (SPI-6) timescales. SPI-3 188 compares the precipitation over a specific 3-month period with the precipitation over the same 189 3-month period for all years included in the dataset; it reflects short-and medium-term 190 moisture conditions and provides a seasonal estimation of precipitation, which is useful for 191 evaluating the available water supply for primary agricultural regions [50]. Similarly,  compares the precipitation over a specific 6-month period with the precipitation over the same   During the peak of the rainy season, almost all of the datasets indicated that the central 227 mountainous part of Central Java had the highest precipitation intensity in the study area. 228 PERSIANN slightly differed by indicating a wider distribution area. CHIRPS, TRMM, and 229 SA-OBS showed good agreement on the precipitation distribution for the study area, with 230 particularly strong agreement between CHIRPS and SA-OBS. During this period, the eight 231 rice-producing districts generally received about 251-400 mm/month of precipitation. 232

Accuracy of the satellite products at describing precipitation
At the peak of the dry season, the precipitation intensity for the entire study area was 233 generally 0-151 mm/month. All datasets indicated that West Java was wetter and East Java 234 was drier with varying spatial distributions. PERSIANN and SA-OBS agreed on the dry 235 conditions of East Java, while CHIRPS and TRMM indicated slightly wetter conditions. 236 CHIRPS, TRMM, and SA-OBS agreed on the wetter conditions of West Java. All rice-237 producing districts in West Java and Cilacap received monthly precipitation of about 100 238 mm/month, while rice-producing districts in eastern Central Java and East Java experienced 239 very low monthly precipitation of <51 mm/month. 240 The peak of the rainy season corresponds to December, January, and February (DJF); the 241 peak of the dry season corresponds to June, July, and August (JJA). 242 extremely dry conditions (SPI-6 of less than −2) in Central Java. All datasets indicated that 283 the meteorological drought had worsened over all of Java by October. Moreover, most of the 284 eight largest rice-producing districts were suffering from the meteorological drought with 285 very dry to extremely dry conditions. The occurrence of an El Niño appears to trigger 286 anomalies in the river flow and reservoir levels of the study area 5-6 months later; this can be 287 used as an early warning for the agricultural sector to take action against potential drought 288 events. 289 The reliability of the satellite products also needed to be evaluated in the temporal 294 domain to determine the suitability of the SPI as a tool for drought monitoring. We focused 295 on the drought characteristics for the eight largest rice-producing districts of Java: Karawang, 14 Java Province; and Ngawi and Lamongan in East Java Province. Fig 5 depicts the time series  298 of the monthly precipitation (left) and SPI-3 (right) of these districts grouped by province: 299 West Java (top), Central Java (middle), and East Java (bottom). The monthly precipitation and 300 SPI-3 were derived from CHIRPS (blue line), TRMM (green line), PERSIANN (red line), and 301 SA-OBS (black line). The time series were obtained for all available periods covered by the 302 datasets. In general, the four datasets showed similar evolutions and variations in precipitation 303 and drought conditions for all locations. Differences were observed in the monthly 304 precipitation intensity; for example, TRMM or PERSIANN sometimes indicated a higher or 305 lower monthly precipitation intensity than the other datasets. Consequently, SPI-3 values 306 from TRMM and PERSIANN sometimes indicated drier or wetter conditions than the other 307 datasets. For certain locations (e.g., West Java and Central Java), PERSIANN sometimes 308 exhibited large deviations or even the opposite trend from the other datasets, which may be 309 due to its algorithms. 310  1982-83, 1997-98, and 2015-16. 339 Other El Niño events were weak or moderate. These strong El Niño events were always 340 followed by meteorological drought events that could be extreme. The peaks of these extreme 341 droughts occur simultaneously with the peaks of ONI with no time lag. The seasonal 342 precipitation patterns persisted even though the intensity was lower than usual during these 343 events. Meteorological droughts with very dry conditions also occurred in 1987, 1992, 1994-344 95, 2007, and 2015 following weak to moderate El Niño events. The meteorological droughts 345 in response to weak and moderate El Niño events did not occur simultaneously, and the 346 16 droughts were moderately to very dry. In 1992, the very dry condition showed a stronger 347 correlation with the IOD timeline rather than El Niño, which peaked later. 348 For the SPI results, the colored bar shows the average from multiple sources, and the lines 349 The severe 2019 drought showed a stronger correlation with the positive-phase IOD, which 370 was the strongest event according to historical records. Based on these results, we concluded 371 that the drought severity depends on the intensity of El Niño and positive-phase IOD 372 occurring simultaneously, while the time phase seems to be modulated by the positive-phase 373

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In this study, we showed that the SPI-based meteorological drought indicator derived 376 from satellite products may be applied to drought monitoring with more detail, wider 377 coverage, and a faster timescale than ground observations. The multi-sources SPI-based 378 meteorological drought indicator described well the relation of meteorological drought 379 characteristics in Java island with El Niño and positive-phase IOD. This adds to a better 380 understanding of the role of the Indian Ocean on drought variability in the study area. Several 381 previous studies have characterized the Java Island drought which is influenced by El Niño in 382 the Pacific Ocean [7,8]. If the SPI or other meteorological drought indicators can be used to 383 predict drought conditions in the next 3 months, then they can be used by the agricultural 384 sector to anticipate and prepare for drought events in the near future. The satellite-based 385 precipitation products can be utilized to predict future conditions including patterns, trends, 386 and seasonal influences for water resource management at different timescales (Murat et al., 387 2018). This may be very useful for farmers, who need to make decisions on their planting 388 strategies while accounting for the possibility of meteorological droughts that can progress to 389 agricultural droughts. This is also important for water management authorities when 390 estimating the water supply and planning a distribution strategy. The proposed satellite-based 391 meteorological drought indicator can help farmers secure their economic livelihood and help 392 the government guarantee food security. For example, rice production can be maintained by 393 Check you referencing style You failed to compared your result with recent and relevant studies, see few suggestions below; https://doi.org/10.1016/j.jenvman.2021.112028 https://doi.org/10.1016/j.jenvman.2021.112112 https://doi.org/10.1007/s10661-020-08730-3 https://doi.org/10. 1080/19376812.2020.1841658 improved water management from several reservoirs and cloud seeding in anticipation of 394 predicted meteorological drought events. 395

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We developed an SPI-based meteorological drought indicator, which we applied to 397 multiple sources of satellite precipitation products for the largest rice-producing districts of 398 Java, which are poorly served by ground observations. The results showed that satellite 399 products can be used to accurately describe the spatial and temporal distributions of 400 meteorological drought events in the study area. A comparison with the ground observation 401 dataset (SA-OBS) showed that the CHIRPS and TRMM had better correlations than 402 PERSIANN of up to 0.6. Meteorological drought characteristics in the study area were shown 403 to be strongly dependent on the variations in El Niño and the positive-phase IOD. The 404 drought severity depends on the intensities of El Niño and positive-phase IOD occurring 405 simultaneously, while the time phase seems to be modulated by the positive-phase IOD. Our 406 results indicate the potential applicability of satellite-based precipitation monitoring to 407 predicting meteorological drought events several months in advance, something valuable for 408 an early warning. Further research is still needed to improve the satellite products accuracy by 409 bias correction implementation [7], as well as investigating the time lag of meteorological 410 drought which will have an impact on causing agricultural drought in the study area. If this is 411 provided, it will be very useful as a decision-making tool for farmers and the government to 412 take action early. 413 The SPI can also be used to indicate wetness in the study area, which seems to be 414 influenced by inter-annual climate variability in the Pacific Ocean and Indian Ocean (i.e., La 415 Niña and negative-phase IOD), although these appear to have different temporal 416 characteristics compared to meteorological droughts. Another study is needed to discuss this 417 topic as it is outside the scope of the present study. 418 No citation is expected in the conclusion, this is your findings and should be solely yours, this section need to be improved, future research statement should be stated.